Database Design

Database design is the process of creating a database schema that is optimized for efficient data storage, retrieval, and manipulation. The design process typically involves several steps, including:

Identify the data requirements: The first step in database design is to identify the data requirements of the system. This involves analyzing the types of data that will be stored in the database, the relationships between the data, and any specific constraints or requirements that must be met.

Create a conceptual data model: Once the data requirements have been identified, a conceptual data model is created to represent the data and its relationships. This model typically includes entities (e.g. customers, orders, products), attributes (e.g. name, price, quantity), and relationships between entities (e.g. one-to-many, many-to-many).

Translate the conceptual data model into a logical data model: The next step is to translate the conceptual data model into a logical data model that is specific to the database management system (DBMS) being used. This involves defining tables, columns, primary keys, foreign keys, and other elements of the database schema.

Normalize the data: Normalization is the process of organizing the data in a database to reduce redundancy and improve data integrity. This involves breaking down large tables into smaller, more specialized tables, and establishing relationships between them.

Optimize for performance: Once the database schema has been designed, it’s important to optimize it for performance. This involves creating indexes, partitions, and other structures that can improve query performance and reduce response times.

Test the database design: Finally, it’s important to thoroughly test the database design to ensure that it meets the requirements of the system and performs as expected. This involves creating test data, running queries, and verifying that the data is stored, retrieved, and manipulated correctly.

Effective database design can have a significant impact on the performance and scalability of a system, and can help to ensure that data is stored and managed in a way that is secure and reliable.

Normalisation

Normalization is the process of organizing data in a database to eliminate redundancy and improve data integrity. The objective of normalization is to minimize data duplication, avoid data inconsistencies, and ensure that data is properly related and linked together.

Normalization typically involves dividing large tables into smaller, more specialized tables and establishing relationships between them. There are several levels of normalization, each with its own set of rules and guidelines:

First Normal Form (1NF): In 1NF, each table must have a primary key and each column in the table must contain atomic values (i.e., values that cannot be further divided).

Second Normal Form (2NF): In 2NF, the table must be in 1NF and all non-key columns in the table must be fully dependent on the primary key.

Third Normal Form (3NF): In 3NF, the table must be in 2NF and all non-key columns in the table must be independent of each other.

There are additional levels of normalization beyond 3NF, including Boyce-Codd Normal Form (BCNF) and Fourth Normal Form (4NF), but these are less commonly used in practice.

Normalization can have several benefits, including:

Improved data integrity: Normalization can help to reduce data redundancy and inconsistencies, ensuring that data is accurate and up-to-date.

Simplified data management: Normalized data is easier to manage and modify, as changes only need to be made in one place rather than multiple locations.

Improved performance: Normalization can improve database performance by reducing the amount of data that needs to be searched and sorted.

However, normalization can also have some drawbacks, including increased complexity and potential performance issues when joining multiple tables together. It’s important to strike a balance between normalization and performance, and to consider the specific requirements of the system being designed.

Functional Dependencies , Normal forms

Functional dependencies (FDs) are a fundamental concept in database design that describes the relationships between attributes in a table. An FD is a constraint between two sets of attributes in a relation, such that the values of one set of attributes determine the values of the other set.

For example, if we have a table with attributes (A, B, C), we can say that there is a functional dependency between A and B if the value of A uniquely determines the value of B. We represent this as A → B.

Normalization is the process of applying a set of rules to a database schema to eliminate redundancy and dependency problems. There are several normal forms, each with its own set of requirements:

First Normal Form (1NF): A relation is in 1NF if it contains only atomic values and there are no repeating groups or arrays.

Second Normal Form (2NF): A relation is in 2NF if it is in 1NF and every non-key attribute is fully functionally dependent on the primary key.

Third Normal Form (3NF): A relation is in 3NF if it is in 2NF and every non-key attribute is non-transitively dependent on the primary key.

Boyce-Codd Normal Form (BCNF): A relation is in BCNF if for every non-trivial FD (A → B), A is a superkey.

Fourth Normal Form (4NF): A relation is in 4NF if it is in BCNF and there are no multi-valued dependencies.

There are also additional normal forms beyond 4NF, such as Fifth Normal Form (5NF), but they are less commonly used.

The higher the normal form, the more normalized and “clean” the database schema is, with less redundancy and fewer dependency problems. However, achieving higher normal forms can sometimes come at the cost of performance and complexity, so it’s important to find a balance that meets the needs of the system being designed.

First Normal Form NF1

First Normal Form (1NF) is a fundamental concept in database design that ensures that a relation contains only atomic values and has no repeating groups or arrays. The goal of 1NF is to eliminate the possibility of repeating groups or arrays in a relation, which can cause data redundancy and make it difficult to update, delete or insert data.

To meet the requirements of 1NF, a relation must satisfy the following conditions:

Every attribute in the relation must have an atomic domain. This means that each attribute value cannot be further decomposed into smaller components.

Every record or tuple in the relation must be unique. This is typically achieved by assigning a unique primary key to each record.

There should be no repeating groups or arrays within the relation. This means that each attribute value should be unique and not be repeated within the same tuple or record.

For example, consider a table with employee information that has a repeating group for skills:

Second Normal Forms NF2

Second Normal Form (2NF) is a database normalization technique used to eliminate data redundancy and improve data integrity in a relational database. To be in 2NF, a table must first meet the requirements of First Normal Form (1NF), which include having atomic values in each column and a unique identifier for each row.

The second requirement for 2NF is that all non-key attributes (i.e., attributes that are not part of the primary key) must be functionally dependent on the entire primary key. This means that if we have a composite primary key consisting of multiple attributes, each non-key attribute should depend on all of those attributes, not just some of them.

To illustrate this, let’s consider an example of a table called “Orders” with columns “OrderID,” “CustomerID,” “CustomerName,” “ProductID,” and “ProductName.” The primary key is a composite key consisting of both “OrderID” and “ProductID.” However, the “CustomerName” attribute depends only on the “CustomerID” attribute and not on the entire composite primary key. Therefore, the “CustomerName” attribute violates the 2NF requirement.

To normalize the table to 2NF, we would split it into two separate tables: “Orders” and “Customers.” The “Orders” table would have columns “OrderID,” “CustomerID,” and “ProductID,” while the “Customers” table would have columns “CustomerID” and “CustomerName.” The “CustomerID” column in the “Orders” table would serve as a foreign key referencing the “Customers” table.

By doing this, we ensure that each attribute is dependent on the entire primary key, eliminating redundancy and improving data integrity in our database.

Third normal forms NF3

Third Normal Form (3NF) is a database normalization technique used to eliminate data redundancy and improve data integrity in a relational database. To be in 3NF, a table must first meet the requirements of First Normal Form (1NF) and Second Normal Form (2NF).

The third requirement for 3NF is that all non-key attributes (i.e., attributes that are not part of the primary key) must be dependent only on the primary key or other non-key attributes, but not on other non-key attributes. This requirement is also known as the “transitive dependency” rule.

To illustrate this, let’s consider an example of a table called “Books” with columns “BookID,” “AuthorID,” “AuthorName,” “PublisherID,” “PublisherName,” and “PublisherCity.” The primary key is the “BookID” column, and the “AuthorID” and “PublisherID” columns are foreign keys referencing the “Authors” and “Publishers” tables, respectively.

However, the “PublisherName” and “PublisherCity” attributes depend only on the “PublisherID” attribute, and not on the entire composite primary key. This violates the 3NF requirement because these attributes are dependent on a non-key attribute (“PublisherID”), which is itself dependent on the primary key (“BookID”).

To normalize the table to 3NF, we would split it into three separate tables: “Books,” “Authors,” and “Publishers.” The “Books” table would have columns “BookID,” “AuthorID,” and “PublisherID,” while the “Authors” and “Publishers” tables would have columns “AuthorID” and “AuthorName,” and “PublisherID,” “PublisherName,” and “PublisherCity,” respectively.

By doing this, we eliminate the transitive dependency between the “PublisherID,” “PublisherName,” and “PublisherCity” attributes, ensuring that each attribute is dependent only on the primary key or other non-key attributes, and not on other non-key attributes. This improves data integrity and eliminates data redundancy in our database.